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High Metabolomic Microdiversity within Co-OccurringIsolates of the Extremely Halophilic BacteriumSalinibacter ruberJosefa Anton1, Marianna Lucio2, Arantxa Pena1*¤, Ana Cifuentes3, Jocelyn Brito-Echeverrıa3,
Franco Moritz2, Dimitrios Tziotis2, Cristina Lopez1, Mercedes Urdiain3, Philippe Schmitt-Kopplin3,4,
Ramon Rossello-Mora3
1 Department of Physiology, Genetics and Microbiology, University of Alicante, Alicante, Spain, 2 Helmholtz Zentrum Munich, German Research Center for Environmental
Health, Analytical BioGeoChemistry, Neuherberg, Germany, 3 Marine Microbiology Group, Departament of Ecology and Marine Resources, Institut Mediterrani d’Estudis
Avancats IMEDEA (CSIC-UIB), Esporles, Illes Balears, Spain, 4 Technische Universitat Munchen, Chair of Analytical Food Chemistry, Freising-Weihenstephan, Germany
Abstract
Salinibacter ruber is an extremely halophilic member of the Bacteroidetes that thrives in crystallizer ponds worldwide. Here,we have analyzed two sets of 22 and 35 co-occurring S. ruber strains, newly isolated respectively, from 100 microliters watersamples from crystalizer ponds in Santa Pola and Mallorca, located in coastal and inland Mediterranean Spain and 350 kmapart from each other. A set of old strains isolated from the same setting were included in the analysis. Genomic andtaxonomy relatedness of the strains were analyzed by means of PFGE and MALDI-TOF, respectively, while their metabolomicpotential was explored with high resolution ion cyclotron resonance Fourier transform mass spectrometry (ICR-FT/MS).Overall our results show a phylogenetically very homogeneous species expressing a very diverse metabolomic pool. Thecombination of MALDI-TOF and PFGE provides, for the newly isolated strains, the same scenario presented by the previousstudies of intra-specific diversity of S. ruber using a more restricted number of strains: the species seems to be veryhomogeneous at the ribosomal level while the genomic diversity encountered was rather high since no identical genomepatterns could be retrieved from each of the samples. The high analytical mass resolution of ICR-FT/MS enabled thedescription of thousands of putative metabolites from which to date only few can be annotated in databases. Somemetabolomic differences, mainly related to lipid metabolism and antibiotic-related compounds, provided enough specificityto delineate different clusters within the co-occurring strains. In addition, metabolomic differences were found between oldand new strains isolated from the same ponds that could be related to extended exposure to laboratory conditions.
Citation: Anton J, Lucio M, Pena A, Cifuentes A, Brito-Echeverrıa J, et al. (2013) High Metabolomic Microdiversity within Co-Occurring Isolates of the ExtremelyHalophilic Bacterium Salinibacter ruber. PLoS ONE 8(5): e64701. doi:10.1371/journal.pone.0064701
Editor: Celine Brochier-Armanet, Universite Claude Bernard - Lyon 1, France
Received February 20, 2013; Accepted April 17, 2013; Published May 31, 2013
Copyright: � 2013 Anton et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the projects CLG2009-12651-C02-01 and 02; and CE-CSD2007-0005 of the Spanish Ministry of Science and Innovation, andall three projects were also co-financed with FEDER support from the European Union. JBE was financed by the Government of the Balearic Islands, Ministry ofEconomy and Finances. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected]
¤ Current address: Department of Biology-Microbiology, University of Balearic Islands, Palma de Mallorca, Illes Balears, Spain
Introduction
Salinibacter ruber is an extremely halophilic bacterium belonging
to the phylum Bacteroidetes that thrives in hypersaline environments.
These systems are dominated by Archaea, such as the square-
shaped Haloquadratum walsbyi and the recently discovered Nano-
haloarchaea [1,2], and harbour the largest number of viruses
reported for aquatic systems. Since its discovery in 1999, S. ruber
has been repeatedly isolated and/or detected in solar salterns and
salt lakes worldwide in places as distant as Australia, California,
the Peruvian Andes, Turkey, Tunisia and Spain [3,4,5,6,7,8].
Many other Bacteroidetes that, according to 16 S rRNA gene based
analysis, cluster with Salinibacter and apart from the rest of
representatives of the group have also been detected in such
environments [5], underpinning the relevance of this phylum as a
main component of the autochthonous extremely halophilic
microbiota in salt-saturated systems. However, so far only a few
extremely halophilic Bacteroidetes have been brought into pure
culture: Salisaeta longa [9], S. ruber, and two new species of
Salinibacter (S. luteus and S. iranicus) isolated from an Iranian salt lake
[10]. In this regard, one of the most striking results of a decade of
studies of Salinibacter in the Mediterranean salterns from which it
was originally isolated has been that all new isolates belonged to
one single species.
Taxonomic studies indicated that all S. ruber isolates were very
similar showing high phylogenetic and phenotypic relatedness [11]
although they displayed a high genomic microdiversity, according
to the high variability of their genomic restriction patterns resolved
by pulsed field gel electrophoresis (PFGE). Indeed, in every new
round of S. ruber isolations from water samples, new PFGE patterns
were observed and in no single instance were the patterns of the
original strains (used for the species description) retrieved again.
When strains from different geographical origins were compared,
these genomic patterns did not provide any sound indication of
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biogeographical segregation within the species, nor did other
genetic comparisons based on RAPD and MLST [11,12].
However, a metabolomic analysis by means of ion cyclotron
resonance Fourier transform mass spectrometry (ICR-FT/MS)
showed that strains of S. ruber isolated from different sites in the
world could be grouped into three geographical areas (Mediter-
ranean, Atlantic and Peruvian Andes) according to their charac-
teristic metabolites [12]. More specifically, components generally
associated with cell membranes, such as fatty acids and terpenoids,
were responsible for the geographic discrimination.
In a subsequent study, an in-depth comparison of the two closest
strains (belonging to the above mentioned Mediterranean group)
was undertaken to get a deeper understanding of S. ruber
microdiversity [13]. Strains M8 and M31, that were isolated from
the same environmental sample in 1999 [14], had identical 16 S
rRNA genes and ITSs and shared around 90% of their genomes
although some hypervariable regions specific of each strain could
be detected, that were enriched in genes coding for sulfotrans-
ferases and glycosyltransferases. Accordingly, metabolomic anal-
yses indicated a consistent difference in sulfonated and glycosy-
lated metabolites within the two strains, mainly in the extracellular
fraction. Thus, two very close members of the same species grown
under the same conditions were expressing different metabolomes.
In order to explore whether this metabolomic diversity was a
general trend within the species, and get an insight of the
biosynthetic potential of S. ruber, we have characterized the
metabolomes of two sets of co-occurring isolates from two salterns
belonging to the same geographical area. Metabolomics of these
strains have been evaluated as a mean of unveiling the structure of
the species and compared with other two well established
taxonomic tools (MALDI-TOF and PFGE).
Overall our results show a phylogenetically very homogeneous
species expressing a very diverse metabolomic pool. The high
analytical mass resolution of ICR-FT/MS enabled the description
of thousands of putative metabolites from which to date only few
can be annotated in databases. Some metabolomic differences,
mainly related to lipid metabolism and antibiotic-related com-
pounds, provided enough specificity to delineate different clusters
within the co-occurring strains.
Materials and Methods
Strain IsolationBrine samples were collected from the salterns of ‘‘Salinas de
Levante’’ in Campos (Mallorca) in July 2006 and ‘‘Bras del Port’’
in Santa Pola (Alicante) in July 2007 (with the permission of the
owners). For the isolation of the autochthonous S. ruber strains
100 ml of the brines were directly plated onto SW 25% media
(containing per litre: 195 g NaCl, 34.6 g MgCl2?6H2O, 49.5 g
MgSO4?7 H2O, 0.72 g CaCl2, 5 g KCl, 0.17 g NaHCO3, 0.65 g
NaBr) emended with 0.1% yeast extract (pH 7.2). Plates were
incubated at 37uC for 2 months until colonies were visible. Single
isolated colonies were picked from the plate, inoculated in 1 ml of
liquid SW 25% with 0.2% yeast extract, and incubated at 37uCwith shaking for one week. Colonies of S. ruber were identified by
PCR with specific primers as previously reported [15]. For this
purpose, DNA extracts were obtained from 100 ml aliquots of
grown cultures by centrifuging, resupending in sterile mQ water
and boiling for 5 minutes. S. ruber isolates were further grown onto
SW 25% plates emended with 0.2% yeast extract. The cultures
were purified by two additional single passes of a single colony to
obtain a pure culture.
Reference StrainsStrains M31T and M8 isolated from ‘‘Salinas de Levante’’ in
Campos (Mallorca), the strains P13 and P18 isolated from Santa
Pola salterns in Alicante in 1999 and the strain IL3 isolated from
Ibiza salterns in 2003 [11,15], were used as controls.
PFGE StudiesIn order to obtain enough biomass for the pulsed field gel
electrophoresis, the new isolates and reference strains were grown
in 20 ml SW 25% emended with 0.2% yeast extract at 37uC until
the OD600 ranged between 0.5 and 0.6. Cells were harvested by
centrifugation in microfuge tubes at 16,000 g, and the supernatant
was discarded. Pellets were washed once with 800 ml of Pett IV
(10 mM Tris.HCl pH 8.0; 3 M NaCl), and resupended in 500 ml
of buffer. The cell suspensions were warmed at 37uC, mixed 1:1
with 1.6% low melting agarose, and solidified in 0.1 ml molds for
15–20 minutes at 4uC. Agarose plugs were incubated overnight at
50uC in ESP buffer (EDTA 0.5 M pH 9–9.5; 1% N-laurylsarco-
sine; 0.5 mg/ml proteinase K). Proteinase K was deactivated by
incubating the plugs in 1.5 M Pefabloc (Roche). Pefabloc was
removed by washing six times for 30 minutes in TE (10 mM Tris-
HCl pH 8.0; 1 mM EDTA pH 8.0). Total DNA was further
digested with 30 U of XbaI (New England Biolabs) in a final
volume of 200 ml following the recommendations of the manu-
facturer.
PFGE was performed in a CHEF-DRII apparatus (BioRad),
using TBE 0.5X buffer, and 1% LE agarose (FMC) gels.
Electrophoreses were run at 14uC using a constant voltage of
6 V/cm and a pulse ramp of 8–12 seconds for 30 h. Low Range
PFGE Marker (New England Biolabs) was used as size standard.
Patterns were compared using the software FPQuest (BioRad).
MALDI-TOF MS AnalysesMatrix assisted laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF MS) analyses and data treatment
were performed as previously reported [16]. The experiments
were performed by the company Anagnostec (Germany) using the
SARAMIS software and databases. Biomass was grown on solid
medium until colonies of, at least, approximately one millimetre in
diameter were visible. For MS analyses, a small amount of biomass
(105 to 106 cells) was transferred, using a sterile pipette tip, to a
FlexiMass stainless steel target. The cells were extracted on the
target with 1 mL of matrix solution, consisting of a saturated
solution of a-cyano-4-hydroxy-cinnamic acid in a mixture of
acetonitrile:ethanol:water (1:1:1) acidified with 3% v/v trifluor-
oacetic acid. The cell suspension in matrix solution was allowed to
evaporate at room temperature and crystal formation was
observed. For each strain, mass spectra were prepared, in
duplicate, and analyzed using an AXIMA Confidence instrument
(Shimadzu/Kratos, Manchester, UK), in linear positive ion
extraction mode. Strain mass spectra were accumulated from
500 shots, derived from five nitrogen laser pulse cycles, scanning
the entire sample spot. Ions were accelerated with pulsed
excitation, with a voltage of 20 kV. Raw mass spectra were
processed automatically for baseline correction and peak recog-
nition. The strain mass spectra profiles have been stored in the
SARAMIS database reference spectra for identification (www.
anagnostec.de, [17]). Samples were run twice with a time lapse of
one week, in order to evaluate profile differences that may occur
upon aging of the cells on the media. Duplicate samples are
indicated by a _02 after the isolate number. The dendrogram of
the mass spectra was obtained by single linkage agglomerate
similarity (similarity matrix of identical peaks) calculations. From
the identical peaks between the strain mass spectra, the similarities
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(%) were calculated and were used to produce the cluster
branches.
Metabolomic AnalysesThe strains were grown in 3 ml of liquid SW 25% emended
with 0.2% yeast extract at 37uC with shaking one week as reported
for previous metabolomics studies of the species [12]. Two batches
were prepared independently: the 22 strains of Mallorca salterns
and the 35 Santa Pola strains, both with the same set of reference
strains. Grown biomass from the two sets of culture batches was
treated as previously published (12) to obtain three different
extracts, extracellular (‘‘SN1’’), cell soluble or intracellular (‘‘SN2’’)
and cell insoluble (‘‘Pellet’’) fractions. For metabolite extractions,
2 mL cell suspensions were centrifuged (16000 g, 2 min at 4uC).
Pelleted biomass was suspended in 1 ml of Milli-Q water and
sonicated to obtain a clear lysate extract. The lysate was acidified
by the addition of 50 mL of 98–100% formic acid. After the
acidification, the clear lysate formed insoluble aggregates that
could be separated from the soluble fraction by centrifugation
(16.000 g, 2 min at 4uC). The clear supernatant (SN2) was stored
for further fractionation, and the insoluble pellet (Pellet) was
resuspended in 500 mL of methanol and stored at 220uC until use.
Both acidified extracellular and cellular soluble fractions were solid
phase extracted using Bond Elut C18 columns (Varian Inc., Lake
Forest, CA, USA). The retained fraction was recovered by the use
of methanol [12].
ICR-FT/MSBroad band mass spectra were acquired on a Bruker (Bremen,
Germany) APEX Qe ICR-FT/MS with a 12 T superconducting
magnet and an Apollo II electrospray (ESI) source in negative and
positive mode. The positive mode was selected as it showed the
highest number of signals with the most annotated signals in the
databases and also differentiated best in the multivariate statistics.
The samples were infused in methanol with the microelectrospray
source at a flow rate of 120 mL h21 with a nebulizer gas pressure
of 20 p.s.i and a drying gas pressure of 15 p.s.i. (200uC). Spectra
were externally calibrated on clusters of arginine (10 mg L21 in
methanol), and calibration errors in the relevant mass range were
always below 100 ppb, which is the prerequisite for an adequate
elementary composition determination up to higher masses.
Relative standard deviation in the intensity values of the peaks
was routinely lower than 5% under our analysis conditions. The
spectra were acquired with a time domain of 1 megaword (where 1
data word corresponds to 32 bits) and a mass range of 150–
2000 m/z. The spectra were zero filled to a processing size of 2
megawords. A sine apodization was performed before Fourier
transformation of the time domain transient. The ion accumula-
tion time in the ion source was set to 0.2 s and 1024 scans were
accumulated for one spectrum.
ICR-FT/MS spectra were exported to peak lists at a signal-to-
noise ratio (S/N = 1) and they were aligned with an in-house
software [18] prior to further analysis. The possible elemental
formulas were calculated for each peak in batch mode by a
software tool written in-house (FORMULAEH). The generated
formulas were validated by setting sensible chemical constraints
(nitrogen rule, atomic oxygen to carbon ratio O/C #(2+C2),
carbon C #100, oxygen 0#80, nitrogen N #5 and sulphur S #1)
[19].
In order to evaluate putative patterns several visualization tools
and multivariate techniques were used. With those it was possible
to reduce the dimensionality of the dataset and extrapolate
informative masses characteristic for the different strains. Several
models were built, starting with unsupervised models as principal
component analysis (PCA) and hierarchical cluster analysis (HCA).
Furthermore, supervised models as partial least square discrimi-
nant analysis (PLS-DA) and orthogonal partial least square
discriminant analysis (OPLS-DA) were used [20,21,22,23]. The
masses with the highest regression coefficients were selected as
discriminative for the different phases [12]. Further extrapolation
of significant masses was done using a non-parametric test
(Wilcoxon test with p,0.05).These lists of masses were evaluated
and assigned with the use of MassTRIX [24,25] and the Japanese
(www.metabolome.jp) metabolome databases. The statistical
analyses were carried out with SIMCA-P 12.0 (Umetrics, Umea,
Sweden), and SAS version 9.1 (SAS Institute Inc., Cary, NC,
USA).
Network analysis was done according to Tziotis et al [26] with a
metabolic ‘‘mass difference list ‘‘at 0.1 ppm edge formation error.
All nodes are mass peaks assigned to empirical formulae with an
error ,0.5 ppm.
Rarefaction Analysis and Diversity IndexesThe PAST software v1.82 b was used to compute the statistical
diversity indexes (Shannon-Weiner) in the sequence dataset.
Rarefaction curves were performed using the Analytic Rarefaction
1.3 available at http://www.uga.edu/strata/software. Good’s
coverage: C = 1 -(ni/nt), where ni is the number of OTUs
observed exactly once and nt is the total number of sequences.
Results and Discussion
Diversity of Isolates Based on Genomic and MALDI-TOFFingerprints
From the plated samples of Santa Pola (SP) and Mallorca (RM),
35 and 22 S. ruber strains, respectively, were isolated. These newly
isolated strains reflected a part of the cultivable population
diversity of members of this species simultaneously occurring in
each saltern at the time of sampling. In order to ascertain the
degree of co-occurring microdiversity within the species at every
location, a polyphasic approach was undertaken that included
PFGE, MALDI-TOF MS and high-resolution metabolomics by
means of ICR-FT/MS.
Intact genomic DNAs from the strains were digested with XbaI
and submitted to PFGE analysis yielding the profiles shown in
Figure 1. The choice of the restriction enzyme was based on the
high GC content (close to 70%) of S. ruber since this enzyme
recognizes the sequence TCTAGA, which is highly infrequent in
high GC genomes. Ten isolates could not be included in the
analyses since their DNA was reproducibly degraded prior to
restriction nuclease treatment, most likely due to endogenous
nuclease activity [27] while others (around 10) could not be
digested with XbaI, likely due to methylation of the target sites (see
below). DNA from some of these strains was digested with the
restriction enzyme SspI, yielding different patterns (data not
shown). All the digested genomes could be resolved using the same
electrophoretic conditions, which indicated their similarity,
although each strain had a distinct restriction pattern. In addition,
there was no clustering of the genomic patterns according to the
place of isolation of strains, and RM and SP strains were mixed
along the groups shown in the similarity dendrogram (Figure 1).
This was also the case for the reference strains, which did not
cluster with the strains isolated from the same environment several
years later. Overall, the new isolates showed a level of similarity
within the range previously observed for members of the species
[11]. Thus, in accordance with the known genomic intraspecific
diversity observed with other cultivated organisms [28], S. ruber
patterns did not resolve any geographical common fingerprint. We
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cannot rule out the possibility that different strains have different
levels of site-specific methylation that could result into different
restriction patterns [29] although most frequently methylation
results in the lack of digestibility, as observed for some strains.
Furthermore, this high genomic (micro)heterogeneity has been
repeatedly observed [5,11] in the species and does not seem to be
artifactual.
In parallel to the genomic approach, a phenotypic character-
ization using MALDI-TOF MS of whole cell extracts was carried
out. This tool provides species-specific macromolecule profiles
[16]. Between 80 and 100 masses ranging from m/z 3000 to
13700 were observed in the 57 strains. The similarity dendrogram
based on the comparison of the fingerprints (Figure S1) showed
that nearly all strains presented identical macromolecule profiles
(about 75% of the strains analyzed), and all of them were grouped
within a similarity of around 80%, a value that that felt within the
range of the expected intraspecies diversity [16]. These results
confirmed that all isolates could be identified as members of the
same species [16], and did not enable us to distinguish any kind of
geographical grouping among the strains. These observations are
not surprising since most of the macromolecules detected are
actually ribosomal proteins with a similar genealogic resolving
power similar to the rRNA [30,31]. However, although molecules
other than those of the ribosome may also influence the observed
phenotype [16], at this level of resolution all strains behaved
identically. Finally, although it was not its main goal, the outcome
of the MALDI-TOF analyses validated the specific method of
isolation and fast identification of S. ruber strains used here.
Thus, the combination of PFGE and MALDI-TOF provides,
for the newly isolated strains, the same scenario presented by the
previous studies of intra-specific diversity of S. ruber using a more
restricted number of strains: the species seems to be very
homogeneous at the ribosomal level while the genomic diversity
encountered was rather high since no identical genome patterns
could be retrieved from each of the samples. In order to explore
this intriguing microdiversity, a detailed metabolomic analysis was
carried out to try to understand the nature of the differences
among the different strains.
Figure 1. Similarity dendrogram (left) comparing the analyzed Salinibacter ruber strains using UPGMA analysis of their XbaI genomicrestriction products separated by PFGE (right). Framed in red, reference strains isolated in 2000 and used here as controls. Stars mark pairs ofclosely related strains from the same origin for comparison with Figure 5.doi:10.1371/journal.pone.0064701.g001
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Metabolomic Analyses: Old Versus New StrainsOur initial studies on the metabolomic-geographical patterns of
S. ruber showed that the isolates of different geographical areas
could be distinguished based on their metabolomic profiles [12].
Here, the goal was to investigate metabolomic similarities and
differences among co-occurring strains as a proxy of the ‘‘meta-
metabolome’’ of the species in the natural environment. In
addition, the analysis was carried out with two sets of isolates from
two different Mediterranean salterns, geographically very close, to
explore the putative differences in metabolite composition that
could be identified as specific of either system.
In spite of the power of spectrometry-based metabolomics in
systems microbiology [32], its use in instraspecific diversity studies
has been very scarce and mostly limited to targeted metabolite
analysis as for e.g. lipids [33]. Krug et al. [34] for instance,
compared the metabolomes of 98 Myxococcus xanthus strains isolated
from 78 locations and including 20 cm-scale isolates from one
location and enabled to identify a number of candidate
compounds, most of them polyketides or nonribosomal peptides,
that greatly exceeded the number of metabolites previously known
as produced by this species.
In this study we describe the metabolite diversity using non-
targeted mass spectrometry based metabolomics with the two sets
of strains (22 RM isolates and 35 SP isolates, respectively) that
were independently cultured; in both cases the same reference
strains (M8, M31T, P13, P18 and IL3; 15) isolated 8 years earlier
were included. For both experimental sets, we used the same
media composition as well as the same culturing conditions (time
Figure 2. Diagrams based on unsupervised PCA analysis of all metabolites present in the cellular soluble fraction of the newisolates (black dots) and old isolates (blue dots) showing the relative homogeneity of the old isolates. (A) PCA of the cellular solublefraction (SN2– SP) of the experimental set of Santa Pola isolates; (B) PCA of the cellular soluble fraction (SN2– RM) of the experimental set of Mallorcaisolates; (C) PCA of the cellular insoluble fraction (Pellet – SP) of the experimental set of Santa Pola isolates (in this case the best distribution wasobserved with the components 3 and 4; and (D) PCA of the cellular insoluble fraction (SN2– RM) of the experimental set of Mallorca isolates.doi:10.1371/journal.pone.0064701.g002
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of incubation, tube type, shaking speed, temperature, etc…). The
analysis of the metabolomic profiles for each strain was carried out
as in previous experiments considering extracellular, intracellular
and methanol extracts of the pellets after centrifugation [12,35].
The chemical space can be described and visualized in
metabolite networks for an overall vision of the global results
and be analyzed in correlation networks (Figure S2A, B)
confirming thus the results of the classical non supervised statistics
[Figure S3] indicating that the three cellular fractions (supernatant
‘‘SN1’’, intracellular soluble ‘‘SN2’’, and intracellular soluble
‘‘Pellet’’) have different molecular fingerprints. The chemical
diversity of 3135 putatively annotated signals is shown in Figure
S2A, where nodes represent masses and connections (edges) are
mass differences which represent potential biochemical reactions.
Signals which were representative for the cellular classes were
colored as follows: extracellular: blue, intracellular: green, pellet:
red. Extracellular compounds cluster in five distinct modules while
two large intracellular modules and three distinct pellet modules
can be observed. One intracellular module is largely mixed with
extracellular compounds, which indicates close compositional
relationships. Both intracellular modules are connected via three
of the extracellular modules and some pellet compounds. This
topology indicates the metabolic probably pathway-based involve-
ment of the cellular classes. The rather apical position of two pellet
compounds underlines the chemical exclusiveness of cell-wall
components. One large extracellular module is rather distant to
the central modules, which indicates exclusive extracellular
chemistry. Thus, Figure S2A indicates two central metabolic
clusters where one is closely associated to ‘cell wall metabolism’
and the other is associated with trans-cell-wall metabolite fluxes.
In Figure S2B we used a correlation network approach in order
to evaluate whether geographical classes as they were verified
through statistical analysis would cluster correspondingly. We
constructed a metabolic correlation network in which the nodes
represent the 186 samples (the three fractions for the 86 strains
analyzed), and the edges represent the positive Pearson correlation
coefficients calculated between the samples (Figure S2B). The
network of Figure S2B was coloured corresponding to Santa Pola
(red), Mallorca (blue) and reference strains (green). The upper
network entirely consists of Pellet samples. Both, the Santa Pola
strains and for Mallorca strains exhibit a two modules in the lower
network. The left module consists exclusively of extracellular
samples, while the right module is composed from intracellular
samples.
In this regard, SN2 and pellet were quite homogeneous for the
analyzed strains, whereas SN1 (extracellular) data were more
Figure 3. Left column: diagrams based on supervised OPLS analyses showing the discrimination of the different isolates old (bluedots) and new (black dots) of the fractions: cellular soluble fraction of Mallorca new isolates SN2-RM (A) and Santa Pola newisolates SN2- SP (B); and cellular insoluble fractions of Mallorca new isolates Pellet-RM (C) and Santa Pola new isolates Pellet- SP(D). Grey dots represent the core metabolome of all isolates that do not offer any discrimination. The selection of the compounds is done taking inaccount the different magnitude of correlation and covariance. The highest value for each list is associated the value 100%. This is the referencepercentage and all other values are scaled accordingly to it. The value that present both value of percentages above 50% have been assumed to be acandidate that can be investigate in MassTRIX, the percentage has been set up as a cut-off of the data.doi:10.1371/journal.pone.0064701.g003
Table 1. Number of metabolites responsible for the discrimination between old (reference strains) and new isolates for eachexperimental set.
Fraction Total metabolome1 Total discriminative masses2 Total annotable masses2Metabolites with assignedpathways
SP (SN2) 5742 410 (7%) 71 (17%) 56
274 (old) 51 (old)
(2705–5077)
136 (new) 20 (new) 39 (old)
SP (PELLET) 4628 661 (14%) 94 (14%)
413 (old) 65 (old) 17 (new)
(2009–2871)
248(new) 29 (new)
RM (SN2) 4889 951 (19%) 149 (16%) 96
508 (old) 76 (old)
(2343–2932)
443 (new) 73 (new 36 (old)
RM (PELLET) 3698 986 (26%) 140 (14%)
674 (old) 73 (old) 60 (new)
(1362–2327)
312 (new) 67 (new)
The number of masses corresponds to those identified as CHONS in where the isotopes had been removed. SN2 refers to the cellular soluble fraction, and PELLET to thecellular insoluble fraction. Annotable masses refer to those whose molecular formula could be assigned to a given metabolite using Salinibacter ruber strains genomicdata.1- Total number of masses in the corresponding fraction for the analyzed strain; in brackets, the range of metabolites found in the different strains.2- In brackets, the percentage of masses of the discriminative metabolome, and the corresponding set of strains set of strains (new) or (old) respectively.doi:10.1371/journal.pone.0064701.t001
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disperse, indicating a higher diversity within this fraction. SN1
includes all the metabolites present in the culture media (that have
been used to different extents by the different strains) and all the
metabolites secreted by the strains to the media. This highly
variable set of metabolites requires the presence (either for import
or export) of a highly diverse pool of transporters. Indeed, this
seems to be the case within the species S. ruber as indicated by the
study of its environmental pangenome [36]. These authors found
that the metagenomic islands of S. ruber (i.e. fragments of the
genome displaying high variation within different members of the
species) were enriched in substrate transporters. Furthermore,
Haloquadratum walsbyi, the archaeon that dominates most of the
environments where S. ruber thrives [37], also presents a rich pool
of transporters within its pangenome, which is also pointing to the
presence in the salterns of a highly heterogeneous dissolved
organic carbon pool, a fact that, albeit indirectly, is also related
with the high metabolomic diversity of SN1.
An astonishingly diverse extracellular metabolome has been
recently detected by means of nanospray desorption electrospray
ionization mass spectrometry when sampling living colonies of
different bacterial genera [38]. In the words by Taxter and Kolter
[39], ‘‘the chemical landscape inhabited and manipulated by
bacteria is vastly more complex and sophisticated than previoulsy
though’’. However, the high metabolomic diversity within the SN1
fraction of the newly isolated S. ruber (13530 ICR-FT/MS features,
possible 7634 elementary compositions), although interesting from
an ecological point of view, hampered any comparative analysis
between the strains since practically each of them behaved in a
specific manner, and no clustering of similarity trends could be
detected among them.
In addition, unsupervised PCA of the metabolomic profiles
indicated that although both experiments were carried out under
the same conditions, the two sets of control strains did not behave
identically (Figure S4). For this reason, both experimental sets
were analyzed independently in order to avoid incongruence.
However, in spite of these differences between the two datasets,
the four generated PCAs (Figure 2) showed consistently that in all
cases the metabolomic profiles from old strains presented an
homogeneity that distinguished them from the rest of the strains
under study. It was remarkable to see that the old strains isolated
from Mallorca (M8 and M31T) were more similar to old isolated
from Santa Pola (P13 and P18) than to new isolates of the same
origin.
In order to retrieve the masses responsible for the discrimination
between old and new strains in each experimental set, we
performed a supervised OPLS analysis (Figure 3). From the
analysis we could observe that the global metabolome for each
fraction and experiment ranged from 3700 to 5700 masses
(Table 1). Between 26% and 7% of the masses were responsible for
the discrimination of old and new isolates metabolomes, whereas
the rest of the metabolites made up the core metabolome of the
strains in study (grey dots in Figure 3). The molecular masses were
transformed to CHONS, and further annotated using the
MassTRIX web server using the KEGG database with the S.
ruber M31T and M8 genomes as reference. From all discriminative
masses, just between 14% and 17% could be annotated (Tables 1
and S1), whereas the rest remained unknown (but not less relevant)
based on the current knowledge on the S. ruber metabolic
pathways. This low proportion of annotable metabolites within
the metabolome is mirrored by the high proportion of hypothetical
proteins encountered when annotating the first microbial genomes
sequenced, both issues being related to database limitations. As
pointed out by Watrons et al. [40], ‘‘most molecules involved in
metabolic exchange are unique to one or a few organisms’’ and
thus there is no currently available neither knowledge nor database
covering this plethora of different metabolites, even for the best
Figure 4. Distribution in the different metabolic classes of the annotable metabolites responsible for the differences between oldand new isolates in each of the analyzed datasets.doi:10.1371/journal.pone.0064701.g004
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known microbes. Only the empirical approach using non-targeted
metabolomics with (ultra)high resolution analytics combined with
adequate mathematics and bioinformatics will enable us to
describe this chemical diversity and unravel, step by step, putative
new structures of secondary or whole classes of metabolite
conjugations never described before.
For both datasets, old strains synthesized the highest number of
discriminative metabolites; in other words, the overall number of
metabolites that could be detected in the cultures was higher for
old than for new strains. When comparing old versus new strains
for each dataset, some metabolic classes were enriched in
discriminative metabolites, although only a few metabolites were
Figure 5. Distance dendrograms showing the similarity groups based on the pairwise comparison of the following metabolomes:cellular soluble (SN2) fraction from Santa Pola strains (A); cellular insoluble (pellet) fraction from Santa Pola strains (C); cellularsoluble (SN2) fraction from Mallorca strains (C); and cellular insoluble (pellet) fraction from Mallorca strains. The blue line indicatesthe 10% clustering threshold (see below) for which no discriminant statistical model could be found to support the groupings observed. The greenline indicates the 40% clustering threshold for which a good statistical support based on metabolite differences could be found. Diagram (B) showsthe rarefaction curves based on distinct clustering threshold. Table (C) shows the correspondence of the Ward distance of the dendrogram with thepercent of clustering of the strains. We take 100% clustering as a Ward distance of 26000. In green it is indicated the 40% threshold for which thegrouping has statistical support, and in blue that of 10% for which no support was found. Some strains are marked with stars for comparison withFigure 1.doi:10.1371/journal.pone.0064701.g005
Metabolomic Microdiversity in Salinibacter ruber
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common to the different subsets of strains (Table S1), which again
underlines the high metabolomic diversity within the species. In
both datasets, the metabolic classes that included the highest
number of metabolites assigned to pathways were lipid metabo-
lism, metabolism of other amino acids, biosynthesis of other
secondary metabolites and metabolism of terpenoids and polyke-
tides. The percentage of discriminative metabolites within each
class was different for Santa Pola and Mallorca datasets, as was
whether old or new strains accumulated more metabolites in a
given class (Figure 4 and Table S1) so it could not be ascertained
that a given class was depleted or enriched in metabolites when
comparing new and old strains.
In both datasets, metabolic pathways (Figure S5) related to
membrane components, and included in the class ‘‘Lipid
metabolism’’, accumulated the highest number of metabolites
discriminating between old and new strains, suggesting that cells
undergo changes in their envelopes as a results of their
‘‘adaptation’’ to laboratory conditions. In addition, some pathways
including metabolites of potentially applied interest, such as
antibiotic or polyketide products, accumulated a relatively high
numbers of annotable discriminative metabolites. This is indicat-
ing that the time of storage of strains under laboratory conditions
may modify their biosynthetic capabilities, a finding that could be
of relevance for the search of new active compounds of
commercial interest out of existing biobanks. In addition,
differences between old and new strains could be due to differences
at the genomic level between old and new strains isolated from the
same ponds, although this would not explain why old strains of
different geographical origins cluster together, as observed here
according to their metabolomics profiles.
Exploring the Metabolomic MicrodiversitySince the old strains used as controls did not show identical
metabolomic profiles in the two experiments, the metabolomic
diversity of new strains from Mallorca and Santa Pola was studied
independently. In order to investigate the putative similarities
among the strains, their metabolite profiles were analyzed as
described in the Material and Methods section. The results
rendered a Ward distance matrix that was used to calculate a
distance dendrogram for each group of strains and cellular fraction
(Figure 5A to 5D). Ward’s method tries to keep minimum the
square error sum when clusters are merged. The different plots
gave different maximal Ward distances between all the strains
(26000, 10000, 16000 and 12000 for SP-SN2, SP-pellet, RM-SN2
and RM-pellet, respectively). In order to compare them, distances
between these four samples were normalized by calculating the
corresponding percentages (see insets in Figure 5A to 5D). Strains
were grouped by setting different distance percentage thresholds
and considering the corresponding clusters of strains as different
OTUs (denoted further in the manuscript as ‘‘metabotype based
OTUs’’: m-OTUs). For each dendrogram in Figures 5A to 5D,
rarefaction curves as well as the Shannon-Weiner diversity and
Good’s coverage indexes were calculated. Rarefaction curves
(insets in Figure 5A to 5D) showed that at 10% clustering distance
the number of m-OTUs already reached a plateau indicating
saturation, in accordance with the Good’s coverage (Figure S6)
that was close to 100% at 40% clustering distance. As expected,
Shannon-Weiner indexes decreased with increasing clustering
distance percentages, approaching 1 already at 40% clustering
distance. These results indicated that the distance range most
appropriate to analyze the metabolic diversity was between 10 and
40%. Further analysis, by using PLS-DA, of the grouping at 10%
(i.e. optimal rarefaction and diversity indexes) and 40% (upper
limit of resolution for diversity studies) showed that only the
clustering at 40% was statistically supported to build classification
models. In other words, there was a statistically solid clustering of
strains at 40% Ward distance, which allowed the identification of
metabolites that could be used to discriminate among the different
clusters of strains. This metabolomic clustering did not show any
relationship with the genomic similarities based on PFGE
restriction patterns, since strains that appeared very close based
on their genomic patterns (marked with stars in Figures 1 and 5),
were not normally members of the same clusters.
In order to retrieve the masses of such ‘‘discriminative’’
metabolites, we took in consideration for each m/z value their
importance in the projection (VIP). These values summarize the
overall contribution of each metabolite to the classification model,
and are considered significant when above 1 [41]. As shown in
Table 2, from 31.2 to 43.1% of the metabolites in the different
fractions were responsible for the observed clustering. Depending
on the fraction considered, from 60 to 70% of the metabolomes,
could thus be considered the core set of metabolites that were
present with the same intensity in the same cellular fraction from
all strains simultaneously isolated from the same pond. As
happened when comparing old versus new strains (see above), only
a relatively small fraction (between 6.9 to 20.5%) of the masses
identified as discriminative could be annotated, whereas the rest of
metabolites were of unknown nature. Around 40% of annotable
Table 2. Number of metabolites responsible for the discrimination among the different metabolic types observed within each setof new strains of Santa Pola and Mallorca.
Fraction Total metabolomeDiscriminative metabolites (%ofthe total)
Annotable metabolites (% of thediscriminative)
Metabolites with assigned pathways(% of the discriminative)
SP (SN2) 5742 1994 (34.7%) 294 (14.7%) 107 (5.4%)
(2705–5077)
SP (PELLET) 4628 1443 (31.2%) 99 (6.9%) 22 (1.5%)
(2009–2871)
RM (SN2) 4889 2106 (43.1%) 358 (17.0%) 124 (5.9%)
(2343–2932)
RM (PELLET) 3698 1551 (41.9%) 318 (20.5%) 112 (7.2%)
(1362–2327)
The candidate discriminative metabolites have a VIP value equal or greater than 1.doi:10.1371/journal.pone.0064701.t002
Metabolomic Microdiversity in Salinibacter ruber
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discriminative metabolites were common to different fractions
from strains from Santa Pola and Mallorca, which indicated that
some common trends could be observed between them in terms of
the ‘‘accessory’’ metabolomes.
The distribution of annotable discriminative metabolites within
metabolic classes was similar for both Mallorca (RM) and Santa
Pola (SP) strains (Figure 6). The classes including the highest
number of metabolites were, in this order, lipid metabolism,
metabolism of other amino acids, biosynthesis of other secondary
metabolites and metabolism of terpenoids and polyketides. These
classes also accumulated the highest number of discriminative
metabolites when old and new strains were compared (see above),
although with a different distribution in the pathways (compare
Figures S5 and S7). Within the lipid metabolism class, the
Figure 6. Distribution in the different metabolic classes of the annotable discriminative metabolites responsible for the clustersshown in Figure S5. Data from all co-occuring strains (A) and from the different fractions (B).doi:10.1371/journal.pone.0064701.g006
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pathways (Figure S7) ether lipid metabolism, fatty acid biosynthe-
sis and sphingolipid metabolism accumulated the highest number
of metabolites responsible for the differences among strain clusters.
Lipid metabolism seemed to be the most versatile metabolic
network in S. ruber, as also indicated by the wide diversity of lipids
found among the discriminative metabolites in other metabolic
classes (data not shown). In addition to the relevance of lipids as
active components of the cell and outer membranes, lipid synthetic
pathways provide precursors for the synthesis of many cellular
components [42].
Within the class ‘‘Metabolism of other amino acids’’, the
pathway cyanoamino acid metabolism had the highest number of
discriminative metabolites, followed by glutathione metabolism.
Some metabolites in the cyanoamino acid pathway are also
annotated as taking part in different lipid metabolism pathways
and are themselves different types of lipids which, again,
underscores the versatility of the lipidome. The cyanoamino acid
pathway is connected to different standard and unusual amino
acid pathways, including sulfur metabolism with glutathione
(GSH), as well as with the nitrogen metabolism pathway. GSH
is a low molecular weight thiol that is involved in bacterial redox
regulation and and adaptation to stressors [43]. The GSH
metabolism pathways includes different amino acids and di and
tri-peptides intermediates that can change their concentrations
depending on the environmental conditions [44].
The class ‘‘Biosynthesis of other secondary metabolites’’
includes many annotated isomers belonging to the biosynthetic
pathways of antibiotics such as novobiocin, puromycin and
penicillin, and cephalosporin. In addition, there is a relatively
high number of metabolites in the biosynthesis pathways of
biosynthesis of type II polyketides (pathways Biosynthesis of type II
polyketides backbone and products, included in the class
‘‘Metabolism of terpenoids and polyketides’’); metabolites derived
from polyketydes are also antibiotics or are involved in defense
mechanisms [45]. However, S. ruber M8 and M31 lack key
(annotable) enzymes in the PKSII pathways which makes this
finding very intriguing and worth of further studies. The
production of antibiotics is a key factor in structuring the social
cohesiveness in ecologically defined bacterial populations in Vibrio
species [46]. Such populations are defined as ‘‘phylogenetic
clusters of closely related but nonclonal individuals which share
common ecological associations’’. Thus, according to this defini-
tion, all the analyzed co-occurring S. ruber isolates studied here are
most likely members of the same ‘‘population’’ although whether
antibiotics and/or signaling compounds are mediating competi-
tion within the population or with potential competitors is
unknown. In any case, antibiotic-related metabolites are important
in structuring the differences among S. ruber strains and further
studies are needed to understand their social behavior on a
molecular basis.
Strain level metabolic diversity. In order to get a closer
look at the metabolomic diversity among different strains, we have
analyzed in detail the metabolomics profiles of strains RM84,
RM101, RM 117, RM131, and RM158, all of them belonging to
the same m-OTU according to their pellet composition (Figure 5).
Although these five strains could not be distinguished by the
statistic tools described above, their metabolite profiles are still
clearly different and include compounds that are present only in a
given strain. For instance, there are 88 annotable metabolites (44
in SN2 and 44 in the pellet fraction) that are only present in RM84
and absent from the rest (data not shown). The high diversity of
such compounds and their unexpected nature are remarkable.
Around one third of the RM84 specific metabolites were lipids but
there were also antibiotic-related products, alkaloids and ana-
logues to phytochemicals, among others. Annotation of metabo-
lites to their isomers is limiting when using (ultra)high resolution
mass spectrometry approaches, which certainly do not enable the
structural identification of all the metabolites present in a complex
sample (although compounds and related pathways can be
proposed with high consistency). Even considering these limita-
tions, the comparison of the chemical diversity on an elementary
composition level clearly shows that very close strains (all the
strains analyzed are in the same m-OTU and were isolated from
the same location) show very different metabolomic potentials.
Therefore, the search for new bioactive substances of pharmaco-
logical and/or biotechnological interest from microbial strains
should not be restricted to the analysis of a single strain of a given
species out of a biobank but rather involve on site sampling taking
advantage of the highest metabolite diversity. This could be shown
as well by an intraspecific metabolomic diversity analysis carried
out with M. xanthus strains [34].
Concluding RemarksThis work shows that very closely S. ruber strains co-occurring in
the same environment and grown under the same lab conditions
are expressing diverse metabolomes. Their comparison provides
some clues to establish clusters within the species, as well as to
separate new strains form the old strains isolated from the same
salterns 8 years before and used for the species description. This
high metabolomic microdiversity within the new isolates, as well as
their diverse PFGE patterns, is very intriguing considering the,
apparently, low opportunities for micro-niche differentiation
offered by the waters from crystallizer ponds, where S. ruber lives.
However, this vision of the water salterns as completely
homogeneous medium can be artifactual given the new view of
the micro-architecture [47] and microbial networks [48] in marine
waters. In addition to these putative spatial discontinuities, S. ruber
strains are exposed in nature to the strain-specific attack of viruses
[13,49] and to changes in environmental conditions due to
seasonal dynamics and saltern operations [50]. The great
metabolomic variability observed among co-occurring individuals
of S. ruber in a small brine volume may be an indication that the
environmental microbial niche diversity is far beyond our current
understanding.
Supporting Information
Figure S1 Dendrogram based on the Maldi-Tof profilesof all strains used in this work. Duplicates analyzed after one
week of culture incubation are indicated by a _2 in the
dendrogram. A group of 3 Halococcus sp. (Pardela 6 to 8) have
been used as out-group for the analyses, as well as some additional
unidentified isolates from S’Avall salterns (Mallorca) had been
used as internal controls. S’Avall saltern isolates are indicated with
the prefix SA
(TIF)
Figure S2 A: Mass Difference Network created from all
annotated data Node Colors: Blue: Extracellular, Green: Intra-
cellular, Red:Pellet. Colors were given only if the frequency of an
m/z peak was 10 fold higher than the average mass frequency
within the other classes. B: Correlation network created at
threshold 0.90. Each nodes represent a sample; the closest the
highest the correlation Regions are labeled as follows: extracellular
blue, intracellular green, and pellet red. This approach individ-
ualized class specific subnetworks confirming thus the PCA
grouping of the sample presented in Figure 2.
(TIF)
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Figure S3 All the data together, in blue the differentcontrol samples and in red the new strains (A) As anexample, the analyses for the Santa Pola isolates areshown: SN1 (B) SN2 (C) and pellet (C). Score scatter plots of
all the isolates (A) from these we spit the data in 3 datasets to
visualize that the dispersion of the control samples in SN1 (B) is
greater than in the other datasets(C, D). 2012.
(TIF)
Figure S4 Unsupervised PCA analysis of each fraction(SN2 (A) and Pellet (B)) taking into account all samplesobtained. Red triangles indicate the new strains of Mallorca, and
the red circles indicate the new strains isolated from Santa Pola. In
blue we have indicated the old strains in the study in where
triangles and circles represent the two different experimental sets
of Mallorca and Santa Pola respectively. Both figures show that
the old strains in both experiments do not behave homogeneously
despite they are the same organisms.
(TIF)
Figure S5 Distribution in the different metabolic path-ways of the annotable metabolites responsible for thedifferences between old and new isolates in each of theanalyzed datasets. Metabolic classes corresponding to the
different pathways are indicated.
(TIF)
Figure S6 Diagrams showing the variability of thediversity Shannon index (A) and the coverage Good’sIndex (B) in relation to the normalized data calculatedfrom the Ward distances given in supplementaryFigures S2 to S3. Data was calculated for each dataset (pellet
and SN2) of both experimental sets (Santa Pola and Mallorca).
The blue bar indicates the 10% dissimilarity clustering threshold
that gives the best compromise between diversity measures
(between 1.5 to 2) and the coverage (around 90%). However
these thresholds did not produce any model for which the
clustering observed was statistically supported. The green bar
indicates the 40% clustering threshold that in all cases produced a
reliable statistical model supporting the clustering observed.
(TIF)
Figure S7 Distribution in the different metabolic path-ways of the annotable discriminative metabolites re-sponsible for the clusters shown in Figure S5. Metabolic
classes corresponding to the different pathways are indicated.
(TIF)
Table S1 Discriminative metabolites found in thedifferent subsets of strains.
(XLSX)
Acknowledgments
We thank Dr. Marıa Gomariz for the help with FPQuest. We are grateful
to the owners of the salterns ‘‘Salinas de Levante’’ and ‘‘Bras del Port’’.
Author Contributions
Conceived and designed the experiments: JA PSK RRM. Performed the
experiments: ML AP AC JBE FM DT CL MU PS. Analyzed the data: JA
ML AP PSK RRM. Contributed reagents/materials/analysis tools: JA
PSK RRM. Wrote the paper: JA PSK RRM.
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PLOS ONE | www.plosone.org 14 May 2013 | Volume 8 | Issue 5 | e64701